Suicide and mental health during the COVID-19 pandemic in Japan

Background The coronavirus disease (COVID-19) pandemic is an unprecedented public health crisis, but its effect on suicide deaths is little understood. Methods We analyzed data from monthly suicide statistics between January 2017 and October 2020 and from online surveys on mental health filled out by the general population in Japan. Results Compared to the 2017–19 period, the number of suicide deaths during the initial phase of the pandemic was lower than average but exceeded the past trend from July 2020. Female suicides, whose numbers increased by approximately 70% in October 2020 (incidence rate ratio: 1.695, 95% confidence interval: 1.558–1.843), were the main source of this increase. The largest increase was found among young women (less than 40 years of age). Our survey data indicated that the status of young women’s mental health has been deteriorating in recent months and that young female workers were more likely to have experienced a job or income loss than any other group, suggesting adverse economic conditions surrounding them. Conclusions Continuous monitoring of mental health, particularly that of the most vulnerable populations identified in this study, and appropriate suicide prevention efforts are necessary during the COVID-19 pandemic.

Suicide and mental health during the COVID-19 pandemic in Japan

BackgroundThe coronavirus disease (COVID-19) pandemic is an unprecedented public health crisis, but its effect on suicide deaths is little understood.MethodsWe analyzed data from monthly suicide statistics between January 2017 and October 2020 and from online surveys on mental health filled out by the general population in Japan.ResultsCompared to the last three years (2017-2019), the number of suicide deaths was lower than average during the initial phase of the pandemic but exceeded the past trend starting in July 2020. The source of the increase was mainly female suicides whose numbers increased by approximately 70% in October 2020 (IRR: 1.695, 95% CI: 1.558-1.843). The largest increase was found among young women (less than 40 years of age). Our survey data indicated that the status of mental health among young women has been deteriorating in recent months, and that young female workers were more likely than any other group to have experienced a job or income loss, suggesting adverse economic conditions surrounding some of these individuals.ConclusionsOur results indicate that continuous monitoring of mental health, particularly that of the most vulnerable populations identified in this study, and appropriate suicide prevention efforts are necessary during the COVID-19 pandemic.

Forecasting End of COVID – 19 in India Based on Time Series Analysis

In this paper, the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models for active and exponential smoothing HOLT for removed rates has been estimated using daily time series data from 1st April to 14thSeptember 2020.The active and removed rates are computed from cumulative confirmed, active, recovered and deceased cases. It has been found that ARIMA (0, 1, 1) and Holt exponential smoothing Models are best fit for active and removed rates respectively. Normalized BIC is 0.577and 0.898 for active and removed rates respectively and is minimum among all the six models considered. Lack of fit of models is tested by Ljung-BoX Q statistic. The pvalue is 0.925 and 0.840 for active and removed rates respectively Since for both the rates p-value is greater than 0.05, hence conclude that our model does not show a lack of fit. On the basis of our analysis, active rate will be nullified latest by 5th January 2020, if everything goes best, as P M of India has assured on eve of Independence Day that vaccine for corona will be available very soon. Otherwise by 9 th February 2021 if the past trend continued and in worst situation it will tends to zero on 26th March 2021. We expect the removed rates will reach 100 percent by 20TH October 2020 if everything goes best and by 5th January 2021 if the past trend continued. On the assumptions that Pandemic will come to an end when removed rate in the population tends to 100 percent and active rate to zero percent. Thus on the basis our analysis we expect that COVID – 19 Pandemic may come to end latest either by 9 th February 2021 or 26th March 2021 subject to condition that the social distance and safely measures remains vigilance to stabilize and control the pandemic and in achieving India’s recovery from COVID-19.

Beyond History: The List of The Most Well Studied Human Protein Structures

Of 20,000 or so canonical human protein sequences, as of July 2020, 6,747 proteins have had their full or partial medium to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the protein database (the PDB) and why? In this paper, we list the 272 top protein structures based on the number of their PDB depositions. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trend and current status for protein science. We briefly discuss the relationship which some of the prominent protein structures have with protein biophysics research and mention their relevance to human diseases. The information may inspire researchers who are new to protein science, but it also provides a year 2020 snap-shot for the state of protein science.

A Literature Review on Machine Learning Applications in Financial Forecasting

Analyzing the past trend and predicting the future movement is an important aspect for every business. Knowing the future value makes an organization more efficient in planning specially if it is related to financial factors. This can be achieved by analyzing the historical data of the company. This is called time series analysis. The increased application of computer and information technologies, has made this more effective and accurate which is called machine learning. Methods of Machine Learning (ML) have been proposed as alternative approach to statistical metthods by many researchers in their academic literature. This paper presents a review of the works where the authors have used machine learning techniques in financial forecasting.

India–Thailand Security Cooperation: Strengthening the Indo-Pacific Resolve

In the past, India’s resolve to connect with countries further to its east centred on its relationship with ASEAN as a group and lacked a holistic outlook as it emphasised on a lopsided approach that left out the security dimension. The bilateral relationship between Bangkok and New Delhi marks an emerging departure from this past trend. In the recent past, Thailand has emerged as a bright spot in India’s vast array of security relationships, with growing focus on maritime security, counterinsurgency and counterterrorism operations, joint patrols and exchange of personnel in training. Besides boosting interoperability, increasing joint actions seek to marry India’s Act East policy with Thailand’s Look West policy, both of which emerged in the past decade of the twentieth century. Both countries look to strengthening their resolve in the Indo-Pacific, even as the region’s stability gets further complicated by sharpening Great Power politics. This article scrutinises the India–Thailand relationship from a security perspective and tests the compatibility of this emerging bilateral relationship with a regional security architecture conceptualisation in the Indo-Pacific. As such, this article seeks to fulfil two important goals: fill the literature deficit in India–Thailand relations that has often been eclipsed and subsequently neglected by the overarching canvass of India–ASEAN relations and analyse India–Thailand bilateral relations from the perspective of an emerging security partnership in the complex labyrinth of relationships in the Indo-Pacific.

Forecasting stock market trend: a comparison of machine learning algorithms

Forecasting the direction of stocks markets has become a popular research topic in recent years. Differentapproaches have been applied by researchers to address the prediction of market trends by consideringtechnical indicators and chart patterns from technical analysis. This paper compares the performanceof four machine learning algorithms to validate the forecasting ability of popular technical indicators inthe technological NASDAQ index. Since the mathematical formulas used in the calculation of technicalindicators comprise historical prices they will be related to the past trend of the market. We assume thatforecasting performance increases when the trend is computed on a longer time horizon. Our resultssuggest that the random forest outperforms the other machine learning algorithms considered in ourresearch, being able to forecast the 10-days ahead market trend, with an average accuracy of 80%.

Past trend shrinks hope for major fall in inequality

Subject Redistributive policies and inequality. Significance Inequality has emerged as an issue of political contention in several advanced and emerging economies, intensifying debates on redistributive policies ranging from taxation of technology firms in Europe to land reform in South Africa. Impacts Institutional resilience is more important to the survival of democratic rule than conflict over redistribution. Redistributive policies have some positive impact on reducing inequality. Countries with proportional representation voting systems appear more likely to elect centre-left parties that redistribute.

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

Refining the Hallstatt Plateau: Short-Term14C Variability and Small Scale Offsets in 50 Consecutive Single Tree-Rings from Southwest Scotland Dendro-Dated to 510–460 BC

Radiocarbon (14C) wiggle-match dating is a technique with a substantial potential to improve the precision of dating timbers in situations where dendrochronology is not tenable. However, one of the key reasons why obtaining a dendrochronological determination might be difficult is the short-lived nature of timbers on a range of archaeological sites, something that also affects the efficiency of the wiggle-match dating technique. Combined with the potential for high expense that the technique presents, it is paramount that wiggle-match dating research design has a good empirical basis. To this end we dated 50 consecutive, individual rings from a timber that grew during the Hallstatt radiocarbon calibration plateau (ca. 750–400 cal BC) in southwest Scotland. The results indicate that (1) the precision and accuracy of wiggle-match dates carried out on short-lived sequences during the Hallstatt plateau may suffer due to insufficient resolution of the calibration data, (2) sampling time-frames roughly equivalent to the underpinning calibration data are recommended (for the period in question this means decadal blocks), and (3) short-lived sequences are at risk of losing accuracy if the actual past trend of radiocarbon diverges from the mean of the radiocarbon calibration curve.